Systems and methods for adjusting the output of a field measurement system to conform to agronomy measurements

ABSTRACT

The present disclosure provides systems and methods for adjusting the output of a field measurement system to conform to agronomy measurements. In particular, the present subject matter is directed to a calibration process and system that uses a calibration model to convert field measurement data expressed according to an automatic system metric into agronomy data that is expressed according to an agronomy metric.

FIELD OF THE INVENTION

The present subject matter relates generally to agricultural fieldmeasurement systems and, more particularly, to systems and methods foradjusting the output of a field measurement system to conform toagronomy measurements.

BACKGROUND OF THE INVENTION

Various types of field measurements provide important data regarding theconditions in a field, including the environmental and/or seed-bedconditions in a field. For example, field measurements can include cropresidue measurements (e.g., percent crop residue cover), soil roughnessmeasurements (e.g., measurements of average soil clod size or the like),and/or other measurements of a field and its characteristics. Thesemeasurements are of high value to agricultural operators to understandcurrent conditions of the field and, if needed, to modify the conditionsof the field to be more optimal.

As one example, for various reasons, it is important to maintain a givenamount of crop residue within a field following an agriculturaloperation. Specifically, crop residue remaining within the field canhelp in maintaining the content of organic matter within the soil andcan also serve to protect the soil from wind and water erosion. However,in some cases, leaving an excessive amount of crop residue within afield can have a negative effect on the soil's productivity potential,such as by slowing down the warming of the soil at planting time and/orby slowing down seed germination. As such, the ability to monitor and/oradjust the amount of crop residue remaining within a field can be veryimportant to maintaining a healthy, productive field, particularly whenit comes to performing tillage operations.

As another example, for various reasons, it is important to maintain agiven amount of soil roughness within a field before or following anagricultural operation. For example, when planting seeds it is generallynot desired to have soil clods that are larger than a certain size.

In the past, these field measurements have been manually generated by ahuman operator/planter. More recently, automatic field measurementsystems have been developed that generate these field measurementsautomatically or in a partially-automated fashion. Typically theseautomatic field measurement systems deploy or otherwise leverage anumber of sensors, such as vision sensors, to produce the fieldmeasurements.

In particular, these automatic field measurement systems typicallyprovide field measurement data expressed according to an automaticsystem metric associated with the automatic field measurement system.However, this automatic system metric may not accurately scale withestablished agronomical measurements. Thus, the output of the automaticfield measurement system may be confusing or otherwise difficult to usedue to its failure to scale accurately with more established agronomymetrics promulgated by various agronomy experts or organizations.

BRIEF DESCRIPTION OF THE INVENTION

Aspects and advantages of the invention will be set forth in part in thefollowing description, or may be obvious from the description, or may belearned through practice of the invention.

One example aspect of the present disclosure is directed to acomputer-implemented method for calibrating automatic fieldmeasurements. The method includes receiving, with a computing device,field measurement data generated by an automatic field measurementsystem, wherein the field measurement data generated by the automaticfield measurement system is expressed according to an automatic systemmetric associated with the automatic field measurement system. Themethod includes accessing, with the computing device, a calibrationmodel that describes a relationship between the automatic system metricassociated with the automatic field measurement system and an agronomymetric. The agronomy metric is different from the automatic systemmetric. The method includes using, with the computing device, thecalibration model to convert the field measurement data expressedaccording to the automatic system metric into agronomy data expressedaccording to the agronomy metric.

Another example aspect of the present disclosure is directed to acomputing system for calibrating automatic field measurements. Thecomputing system includes one or more processors and one or morenon-transitory computer-readable media that collectively store acalibration model that describes a relationship between an automaticsystem metric associated with an automatic field measurement system andan agronomy metric. The agronomy metric is different from the automaticsystem metric. The one or more non-transitory computer-readable mediacollectively store instructions that, when executed by the one or moreprocessors, cause the computing system to perform operations. Theoperations include receiving field measurement data generated by theautomatic field measurement system. The field measurement data generatedby the automatic field measurement system is expressed according to theautomatic system metric associated with the automatic field measurementsystem. The operations include accessing the calibration model thatdescribes the relationship between the automatic system metricassociated with the automatic field measurement system and the agronomymetric. The operations include using the calibration model to convertthe field measurement data expressed according to the automatic systemmetric into agronomy data expressed according to the agronomy metric.

Another example aspect of the present disclosure is directed to anagricultural work vehicle or agricultural implement that includes anautomatic field measurement system configured to generate fieldmeasurement data descriptive of an agricultural field. The fieldmeasurement data generated by the automatic field measurement system isexpressed according to an automatic system metric associated with theautomatic field measurement system. The agricultural work vehicle oragricultural implement includes a calibration computing systemconfigured to: receive the field measurement data generated by theautomatic field measurement system; access a calibration model thatdescribes a relationship between the automatic system metric associatedwith the automatic field measurement system and an agronomy metric,wherein the agronomy metric is different from the automatic systemmetric; and use the calibration model to convert the field measurementdata expressed according to the automatic system metric into agronomydata expressed according to the agronomy metric.

These and other features, aspects and advantages of the presentinvention will become better understood with reference to the followingdescription and appended claims. The accompanying drawings, which areincorporated in and constitute a part of this specification, illustrateembodiments of the invention and, together with the description, serveto explain the principles of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

A full and enabling disclosure of the present invention, including thebest mode thereof, directed to one of ordinary skill in the art, is setforth in the specification, which makes reference to the appendedfigures, in which:

FIG. 1 illustrates a perspective view of one embodiment of a workvehicle towing an implement in accordance with aspects of the presentsubject matter;

FIG. 2 illustrates a perspective view of the implement shown in FIG. 1;

FIG. 3 illustrates a schematic view of one embodiment of a computingsystem in accordance with aspects of the present subject matter;

FIG. 4 illustrates a schematic view of one embodiment of a computingsystem in accordance with aspects of the present subject matter;

FIG. 5 illustrates a flow diagram of one embodiment of a method forcalibrating automatic field measurements in accordance with aspects ofthe present subject matter.

DETAILED DESCRIPTION OF THE INVENTION

Reference now will be made in detail to embodiments of the invention,one or more examples of which are illustrated in the drawings. Eachexample is provided by way of explanation of the invention, notlimitation of the invention. In fact, it will be apparent to thoseskilled in the art that various modifications and variations can be madein the present invention without departing from the scope or spirit ofthe invention. For instance, features illustrated or described as partof one embodiment can be used with another embodiment to yield a stillfurther embodiment. Thus, it is intended that the present inventioncovers such modifications and variations as come within the scope of theappended claims and their equivalents.

In general, the present subject matter is directed to systems andmethods for adjusting the output of a field measurement system toconform to agronomy measurements. In particular, the present subjectmatter is directed to a calibration process and system that uses acalibration model to convert field measurement data expressed accordingto an automatic system metric into agronomy data that is expressedaccording to an agronomy metric.

More particularly, as described above, an automatic field measurementsystem can produce field measurement data that is expressed according toan automatic system metric associated with the automatic fieldmeasurement system. As examples, the field measurement data can includecrop residue data, soil roughness data, and/or other measurements offield conditions or characteristics.

However, this automatic system metric may not accurately scale withestablished agronomical measurements. For example, the automatic systemmay output crop residue in a logarithmic scale while an agronomistmeasures in a linear scale. Thus, the output of the automatic fieldmeasurement system may be confusing or otherwise difficult to use due toits failure to scale accurately with more established agronomy metricspromulgated by various agronomy experts or organizations.

As such, the present disclosure provides systems and methods thatperform a calibration from one scale to another. In particular, in oneexample, a computing system can receive field measurement data generatedby an automatic field measurement system and expressed according to anautomatic system metric associated with the automatic field measurementsystem. The computing system can access a calibration model thatdescribes a relationship between the automatic system metric associatedwith the automatic field measurement system and an agronomy metric,wherein the agronomy metric is different from the automatic systemmetric. The computing system can use the calibration model to convertthe field measurement data expressed according to the automatic systemmetric into agronomy data expressed according to the agronomy metric.

The calibration model used by the systems and methods of the presentdisclosure can have a number of different forms or structures. As oneexample, the calibration model can perform or include a least squarespolynomial fit that describes the relationship between the automaticsystem metric and the agronomy metric according to a polynomialexpression. As another example, the calibration model can include or usea look up table that provides an agronomy data value for each possiblefield measurement data value. As yet another example, the calibrationmodel can perform or include a Gaussian Process regression thatdescribes the relationship between the automatic system metric and theagronomy metric As another example, the calibration model can be orinclude a spline or otherwise piece-wise function that describes therelationship between the automatic system metric and the agronomymetric. As yet another example, the calibration model can convert fieldmeasurement data expressed according to a logarithmic scale intoagronomy data expressed according to a linear scale.

Thus, in some embodiments, the calibration process can be combined withan existing field measurement system or algorithm. In other embodiments,the calibration process can be co-developed with a field measurementalgorithm. By removing the agronomy calibration out of such fieldmeasurement algorithm (that is, separating the two problems and allowingthe calibration process to handle agronomy calibration) the developmentof the field measurement algorithm can be greatly simplified, therebylikely leading to superior results. Furthermore, existing or newlydeveloped field measurement algorithms (which may, for example, beanalytical or heuristic algorithms) may exhibit bias in the algorithms(e.g., due to bias in the underlying data). Thus, in some embodiments,use of the calibration layer may also be able to correct or account forbias exhibited by the underlying field measurement algorithm. As such,in some embodiments, the calibration system and process can be viewed asan additional calibration layer that recalibrates a measurementalgorithm that is otherwise accurate.

In some embodiments, the calibration systems and methods of the presentdisclosure can convert the field measurement data according to one of anumber of different agronomy metrics. For example, a plurality ofdifferent agronomy metrics may be promulgated by different agronomistsor agronomy organizations and certain of such different agronomy metricsmay be preferred or used by some agricultural operators while othermetrics are preferred or used by other agricultural operators. In someembodiments, a user can select one of such plurality of differentagronomy metrics and the calibration system can convert the fieldmeasurement data into agronomy data expressed according to or inaccordance with the selected agronomy metric.

In some embodiments, the calibration systems and methods of the presentdisclosure can convert the field measurement data according to one of anumber of different agronomy metrics that are specifically associatedwith different geographic areas and/or different soil types or materials(e.g., that are associated with the different geographic areas). Forexample, a plurality of different agronomy metrics may be promulgated,where each agronomy metric is designed for or otherwise specific to adifferent geographic area and/or different mix of soil types ormaterials. In some embodiments, a user can select one of such pluralityof different agronomy metrics and the calibration system can convert thefield measurement data into agronomy data expressed according to or inaccordance with the selected agronomy metric. In other embodiments, thecalibration system can automatically select the particular agronomymetric into which the field measurement data is converted based onavailable information including, for example, location information(e.g., GPS data), humidity information, date or time of day information,imagery of the soil, and/or other types of data.

Furthermore, in some embodiments, an operation of an agricultural workvehicle or implement can be controlled based at least in part on theagronomy data. For example, the automatic field measurement systemand/or the calibration computing device can be physically locatedon-board the at least one of the work vehicle or the implement and thecalibration can be performed in real-time as part of a control feedbackloop that controls operations of the vehicle or implement in real-timebased on the field measurements expressed according to the agronomymetric. As examples, the relative positioning, penetration depth, downforce, and/or any other operational parameters associated with one ormore ground-engaging tools can be modified based on the agronomy data,thereby modifying the field conditions within the field towards a targetcondition. Thus, the systems and methods of the present disclosure canenable improved real-time control that measures and accounts forexisting field conditions during field operations.

Through the use of a calibration model to convert field measurementsinto agronomy data, the systems and methods of the present disclosurecan produce field measurements that are more easily understandable tooperators of automatic field measurement systems. For example, thecalibration process can be performed to enable automatic fieldmeasurements to scale appropriately with agronomy metrics with which theagricultural operator may be more familiar or with which existingimplements or systems may be designed to operate, thereby enablingimproved integration of the automatic field measurement system withexisting systems or structures. This improved understandability andintegration can enable improved and/or more precise control of the workvehicle and/or implement to obtain a desired field condition (e.g.,residue cover and/or soil roughness) within a field and, as a result,lead to superior agricultural outcomes.

Referring now to drawings, FIGS. 1 and 2 illustrate perspective views ofone embodiment of a work vehicle 10 and an associated agriculturalimplement 12 in accordance with aspects of the present subject matter.Specifically, FIG. 1 illustrates a perspective view of the work vehicle10 towing the implement 12 (e.g., across a field). Additionally, FIG. 2illustrates a perspective view of the implement 12 shown in FIG. 1. Asshown in the illustrated embodiment, the work vehicle 10 is configuredas an agricultural tractor. However, in other embodiments, the workvehicle 10 may be configured as any other suitable agricultural vehicle.

As particularly shown in FIG. 1, the work vehicle 10 includes a pair offront track assemblies 14, a pair of rear track assemblies 16 and aframe or chassis 18 coupled to and supported by the track assemblies 14,16. An operator's cab 20 may be supported by a portion of the chassis 18and may house various input devices for permitting an operator tocontrol the operation of one or more components of the work vehicle 10and/or one or more components of the implement 12. Additionally, as isgenerally understood, the work vehicle 10 may include an engine 22 (FIG.3) and a transmission 24 (FIG. 3) mounted on the chassis 18. Thetransmission 24 may be operably coupled to the engine 22 and may providevariably adjusted gear ratios for transferring engine power to the trackassemblies 14, 16 via a drive axle assembly (not shown) (or via axles ifmultiple drive axles are employed).

Moreover, as shown in FIGS. 1 and 2, the implement 12 may generallyinclude a carriage frame assembly 30 configured to be towed by the workvehicle via a pull hitch or tow bar 32 in a travel direction of thevehicle (e.g., as indicated by arrow 34). The carriage frame assembly 30may be configured to support a plurality of ground-engaging tools, suchas a plurality of shanks, disk blades, leveling blades, basketassemblies, and/or the like. In several embodiments, the variousground-engaging tools may be configured to perform a tillage operationacross the field along which the implement 12 is being towed.

As particularly shown in FIG. 2, the carriage frame assembly 30 mayinclude aft extending carrier frame members 36 coupled to the tow bar32. In addition, reinforcing gusset plates 38 may be used to strengthenthe connection between the tow bar 32 and the carrier frame members 36.In several embodiments, the carriage frame assembly 30 may generallyfunction to support a central frame 40, a forward frame 42 positionedforward of the central frame 40 in the direction of travel 34 of thework vehicle 10, and an aft frame 44 positioned aft of the central frame40 in the direction of travel 34 of the work vehicle 10. As shown inFIG. 2, in one embodiment, the central frame 40 may correspond to ashank frame configured to support a plurality of ground-engaging shanks46. In such an embodiment, the shanks 46 may be configured to till thesoil as the implement 12 is towed across the field. However, in otherembodiments, the central frame 40 may be configured to support any othersuitable ground-engaging tools.

Additionally, as shown in FIG. 2, in one embodiment, the forward frame42 may correspond to a disk frame configured to support various gangs orsets 48 of disk blades 50. In such an embodiment, each disk blade 50may, for example, include both a concave side (not shown) and a convexside (not shown). In addition, the various gangs 48 of disk blades 50may be oriented at an angle relative to the travel direction 34 of thework vehicle 10 to promote more effective tilling of the soil. However,in other embodiments, the forward frame 42 may be configured to supportany other suitable ground-engaging tools.

As another example, ground-engaging tools can include harrows which caninclude, for example, a number of tines or spikes, which are configuredto level or otherwise flatten any windrows or ridges in the soil. Theimplement 12 may include any suitable number of harrows. In fact, someembodiments of the implement 12 may not include any harrows.

In some embodiments, the implement 12 may optionally include one or moreadditional ground-engaging tools, such as one or more basket assembliesor rotary firming wheels. The baskets may be configured to reduce thenumber of clods in the soil and/or firm the soil over which theimplement 12 travels. Each basket may be configured to be pivotallycoupled to one of the frames 40, 42, 44, or other components of theimplement 12. It should be appreciated that the implement 12 may includeany suitable number of baskets. In fact, some embodiments of theimplement 12 may not include any baskets.

Moreover, similar to the central and forward frames 40, 42, the aftframe 44 may also be configured to support a plurality ofground-engaging tools. For instance, in the illustrated embodiment, theaft frame is configured to support a plurality of leveling blades 52 androlling (or crumbler) basket assemblies 54. However, in otherembodiments, any other suitable ground-engaging tools may be coupled toand supported by the aft frame 44, such as a plurality of closing disks.

In addition, the implement 12 may also include any number of suitableactuators (e.g., hydraulic cylinders) for adjusting the relativepositioning, penetration depth, and/or down force associated with thevarious ground-engaging tools (e.g., ground-engaging tools 46, 50, 52,54). For instance, the implement 12 may include one or more firstactuators 56 coupled to the central frame 40 for raising or lowering thecentral frame 40 relative to the ground, thereby allowing thepenetration depth and/or the down pressure of the shanks 46 to beadjusted. Similarly, the implement 12 may include one or more secondactuators 58 coupled to the disk forward frame 42 to adjust thepenetration depth and/or the down pressure of the disk blades 50.Moreover, the implement 12 may include one or more third actuators 60coupled to the aft frame 44 to allow the aft frame 44 to be movedrelative to the central frame 40, thereby allowing the relevantoperating parameters of the ground-engaging tools 52, 54 supported bythe aft frame 44 (e.g., the down pressure and/or the penetration depth)to be adjusted.

It should be appreciated that the configuration of the work vehicle 10described above and shown in FIG. 1 is provided only to place thepresent subject matter in an exemplary field of use. Thus, it should beappreciated that the present subject matter may be readily adaptable toany manner of work vehicle configuration. For example, in an alternativeembodiment, a separate frame or chassis may be provided to which theengine, transmission, and drive axle assembly are coupled, aconfiguration common in smaller tractors. Still other configurations mayuse an articulated chassis to steer the work vehicle 10, or rely ontires/wheels in lieu of the track assemblies 14, 16.

It should also be appreciated that the configuration of the implement 12described above and shown in FIGS. 1 and 2 is only provided forexemplary purposes. Thus, it should be appreciated that the presentsubject matter may be readily adaptable to any manner of implementconfiguration. For example, as indicated above, each frame section ofthe implement 12 may be configured to support any suitable type ofground-engaging tools, such as by installing closing disks on the aftframe 44 of the implement 12 or other modifications.

Additionally, in accordance with aspects of the present subject matter,the work vehicle 10 and/or the implement 12 may include or haveassociated therewith an automatic field measurement system. Theautomatic field measurement system can automatically or in an at leastpartially-automated fashion generate field measurement data thatincludes measurements of one or more conditions of a field. Exampleconditions of a field include crop residue conditions (e.g., percentcrop residue cover), soil roughness conditions (e.g., average soil clodsize, clod density, or other roughness characteristics), and/or othermeasures of various conditions or characteristics of a field.

Generally, the automatic field measurement system can include one ormore sensors (e.g., as shown at 121 in FIG. 3) that generate data thatcan be processed to measure the field conditions. The sensors can be anytype of sensors including depth sensors, humidity sensors, temperaturesensors, surface roughness sensors, vision sensors such as imagingdevice, acoustic sensors, and/or other types of sensors.

Thus, in some embodiments, as illustrated in FIGS. 1 and 2, the workvehicle 10 and/or the implement 12 may have one or more imaging devicescoupled thereto and/or supported thereon for capturing images or otherimage data associated with the field as an operation is being performedvia the implement 12. Specifically, in several embodiments, the imagingdevice(s) may be provided in operative association with the work vehicle10 and/or the implement 12 such that the imaging device(s) has a fieldof view directed towards a portion(s) of the field disposed in front of,behind, and/or underneath some portion of the work vehicle 10 and/orimplement 12 such as, for example, alongside one or both of the sides ofthe work vehicle 10 and/or the implement 12 as the implement 12 is beingtowed across the field. As such, the imaging device(s) may captureimages from the tractor 10 and/or implement 12 of one or more portion(s)of the field being passed by the tractor 10 and/or implement 12.

In general, the imaging device(s) may correspond to any suitabledevice(s) configured to capture images or other image data of the fieldthat allow the field's soil to be distinguished from the crop residueremaining on top of the soil. For instance, in several embodiments, theimaging device(s) may correspond to any suitable camera(s), such assingle-spectrum camera or a multi-spectrum camera configured to captureimages, for example, in the visible light range and/or infrared spectralrange. Additionally, in a particular embodiment, the camera(s) maycorrespond to a single lens camera configured to capture two-dimensionalimages or a stereo camera(s) having two or more lenses with a separateimage sensor for each lens to allow the camera(s) to capturestereographic or three-dimensional images. Alternatively, the imagingdevice(s) may correspond to any other suitable image capture device(s)and/or vision system(s) that is capable of capturing “images” or otherimage-like data that allow the crop residue existing on the soil to bedistinguished from the soil. For example, the imaging device(s) maycorrespond to or include radio detection and ranging (RADAR) sensorsand/or light detection and ranging (LIDAR) sensors.

It should be appreciated that work vehicle 10 and/or implement 12 mayinclude any number of imaging device(s) 104 or other sensors provided atany suitable location that allows images of the field to be captured asthe vehicle 10 and implement 12 traverse through the field. Forinstance, FIGS. 1 and 2 illustrate examples of various locations formounting one or more imaging device(s) for capturing images of thefield. Specifically, as shown in FIG. 1, in one embodiment, one or moreimaging devices 104A may be coupled to the front of the work vehicle 10such that the imaging device(s) 104A has a field of view 106 that allowsit to capture images of an adjacent area or portion of the fielddisposed in front of the work vehicle 10. For instance, the field ofview 106 of the imaging device(s) 104A may be directed outwardly fromthe front of the work vehicle 10 along a plane or reference line thatextends generally parallel to the travel direction 34 of the workvehicle 10. In addition to such imaging device(s) 104A (or as analternative thereto), one or more imaging devices 104B may also becoupled to one of the sides of the work vehicle 10 such that the imagingdevice(s) 104B has a field of view 106 that allows it to capture imagesof an adjacent area or portion of the field disposed along such side ofthe work vehicle 10. For instance, the field of view 106 of the imagingdevice(s) 104B may be directed outwardly from the side of the workvehicle 10 along a plane or reference line that extends generallyperpendicular to the travel direction 34 of the work vehicle 10.

Similarly, as shown in FIG. 2, in one embodiment, one or more imagingdevices 104C may be coupled to the rear of the implement 12 such thatthe imaging device(s) 104C has a field of view 106 that allows it tocapture images of an adjacent area or portion of the field disposed aftof the implement. For instance, the field of view 106 of the imagingdevice(s) 104C may be directed outwardly from the rear of the implement12 along a plane or reference line that extends generally parallel tothe travel direction 34 of the work vehicle 10. In addition to suchimaging device(s) 104C (or as an alternative thereto), one or moreimaging devices 104D may also be coupled to one of the sides of theimplement 12 such that the imaging device(s) 104D has a field of view106 that allows it to capture images of an adjacent area or portion ofthe field disposed along such side of the implement 12. For instance,the field of view 106 of the imaging device 104D may be directedoutwardly from the side of the implement 12 along a plane or referenceline that extends generally perpendicular to the travel direction 34 ofthe work vehicle 10.

It should be appreciated that, in alternative embodiments, the imagingdevice(s) 104 may be installed at any other suitable location thatallows the device(s) to capture images of an adjacent portion of thefield, such as by installing an imaging device(s) at or adjacent to theaft end of the work vehicle 10 and/or at or adjacent to the forward endof the implement 12. It should also be appreciated that, in severalembodiments, the imaging devices 104 may be specifically installed atlocations on the work vehicle 10 and/or the implement 12 to allow imagesto be captured of the field both before and after the performance of afield operation by the implement 12. For instance, by installing theimaging device 104A at the forward end of the work vehicle 10 and theimaging device 104C at the aft end of the implement 12, the forwardimaging device 104A may capture images of the field prior to performanceof the field operation while the aft imaging device 104C may captureimages of the same portions of the field following the performance ofthe field operation.

Referring now to FIGS. 3 and 4, schematic views of embodiments of acomputing system 100 are illustrated in accordance with aspects of thepresent subject matter. In general, the system 100 will be describedherein with reference to the work vehicle 10 and the implement 12described above with reference to FIGS. 1 and 2. However, it should beappreciated that the disclosed system 100 may generally be utilized withwork vehicles having any suitable vehicle configuration and/orimplements have any suitable implement configuration.

In several embodiments, the system 100 may include a controller 102 andvarious other components configured to be communicatively coupled toand/or controlled by the controller 102, such as one or more sensors 121and/or various components of the work vehicle 10 and/or the implement12. In some embodiments, the controller 102 is physically coupled to thework vehicle 10 and/or the implement 12. In other embodiments, thecontroller 102 is not physically coupled to the work vehicle 10 and/orthe implement 12 (e.g., remotely located from the work vehicle 10 and/orthe implement 12) and instead may communicate with the work vehicle 10and/or the implement 12 over a wireless network.

As will be described in greater detail below, the controller 102 may beconfigured to generate automatic field measurements and convert theautomatic field measurements to agronomy data. In particular, FIG. 3illustrates a computing environment in which the controller 102 canoperate to generate measurement data 120 based on sensor data newlyreceived from one or more sensors 121. That is, FIG. 3 illustrates acomputing environment in which the controller 102 is actively used inconjunction with a work vehicle and/or implement (e.g., during operationof the work vehicle and/or implement within a field). As will bediscussed further below, FIG. 4 depicts a computing environment in whichthe controller 102 can communicate over a network 180 with a machinelearning computing system 150 to train and/or receive a calibrationmodel 128. Thus, FIG. 4 illustrates operation of the controller 102 totrain a calibration model 128 and/or to receive a trained calibrationmodel 128 from a machine learning computing system 150 (e.g., FIG. 4shows the “training stage”) while FIG. 3 illustrates operation of thecontroller 102 to use the calibration model 128 to actively convertautomatic field measurement data to agronomy data (e.g., FIG. 3 shows“inference stage”). However, as noted elsewhere herein, the calibrationmodel 128 is not required to be a machine-learned model and may, in someembodiments, be a “hand-crafted” heuristic or impose a hand-craftedrelationship.

Referring first to FIG. 3, in general, the controller 102 may correspondto any suitable processor-based device(s), such as a computing device orany combination of computing devices. Thus, as shown in FIG. 3, thecontroller 102 may generally include one or more processor(s) 110 andassociated memory devices 112 configured to perform a variety ofcomputer-implemented functions (e.g., performing the methods, steps,algorithms, calculations and the like disclosed herein). As used herein,the term “processor” refers not only to integrated circuits referred toin the art as being included in a computer, but also refers to acontroller, a microcontroller, a microcomputer, a programmable logiccontroller (PLC), an application specific integrated circuit, and otherprogrammable circuits. Additionally, the memory 112 may generallycomprise memory element(s) including, but not limited to, computerreadable medium (e.g., random access memory (RAM)), computer readablenon-volatile medium (e.g., a flash memory), a floppy disk, a compactdisc-read only memory (CD-ROM), a magneto-optical disk (MOD), a digitalversatile disc (DVD) and/or other suitable memory elements. Such memory112 may generally be configured to store information accessible to theprocessor(s) 110, including data 114 that can be retrieved, manipulated,created and/or stored by the processor(s) 110 and instructions 116 thatcan be executed by the processor(s) 110.

In several embodiments, the data 114 may be stored in one or moredatabases. For example, the memory 112 may include a sensor datadatabase 118 for storing sensor data received from the sensors 121. Forexample, the sensors 121 may be configured to continuously orperiodically generate sensor data descriptive of adjacent portion(s) ofthe field as an operation is being performed with the field. In such anembodiment, the sensor data transmitted to the controller 102 from thesensors 121 may be stored within the sensor data database 118 forsubsequent processing and/or analysis. It should be appreciated that, asused herein, the term sensor data may include any suitable type of datareceived from sensors, including image data received from the imagingdevice(s) 104 that allows for the crop residue coverage of a field to beanalyzed, including photographs and other image-related data (e.g., scandata and/or the like).

Additionally, as shown in FIG. 3, the memory 12 may include ameasurement data database 120 for storing measurement data (e.g.,automatic field measurement data and/or converted agronomy data) for thefield being processed. For example, as indicated above, based on thesensor data received from the sensors 121, the controller 102 may beconfigured to generate field measurement data associated with the fieldand can re-calibrate the field measurement data to agronomy data thatcomplies with one or more agronomy metrics. The field measurement dataand/or the agronomy data may be stored within the measurement datadatabase 120 for subsequent processing, use, and/or analysis.

Moreover, in several embodiments, the memory 12 may also include alocation database 122 storing location information about the workvehicle/implement 10, 12 and/or information about the field beingprocessed (e.g., a field map). Specifically, as shown in FIG. 3, thecontroller 102 may be communicatively coupled to a positioning device(s)124 installed on or within the work vehicle 10 and/or on or within theimplement 12. For example, in one embodiment, the positioning device(s)124 may be configured to determine the exact location of the workvehicle 10 and/or the implement 12 using a satellite navigation positionsystem (e.g. a GPS system, a Galileo positioning system, the GlobalNavigation satellite system (GLONASS), the BeiDou Satellite Navigationand Positioning system, and/or the like). In such an embodiment, thelocation determined by the positioning device(s) 124 may be transmittedto the controller 102 (e.g., in the form coordinates) and subsequentlystored within the location database 122 for subsequent processing and/oranalysis.

Additionally, in several embodiments, the location data stored withinthe location database 122 may also be correlated to the sensor datastored within the sensor data database 118. For instance, in oneembodiment, the location coordinates derived from the positioningdevice(s) 124 and the sensor data captured by the sensors 121 may bothbe time-stamped. In such an embodiment, the time-stamped data may allowsensor data captured by the sensors 121 to be matched or correlated to acorresponding set of location coordinates received from the positioningdevice(s) 124, thereby allowing the precise location of the portion ofthe field described by a given set of sensor data to be known (or atleast capable of calculation) by the controller 102.

Moreover, by matching sensor data to a corresponding set of locationcoordinates, the controller 102 may also be configured to generate orupdate a corresponding field map associated with the field beingprocessed. For example, in instances in which the controller 102 alreadyincludes a field map stored within its memory 112 that includes locationcoordinates associated with various points across the field, sensor datacaptured by the imaging device(s) 104 may be mapped or correlated to agiven location within the field map. Alternatively, based on thelocation data and the associated sensor data, the controller 102 may beconfigured to generate a field map for the field that includes thegeo-located sensor data associated therewith.

Referring still to FIG. 3, in several embodiments, the instructions 116stored within the memory 112 of the controller 102 may be executed bythe processor(s) 110 to implement a measurement analysis module 126. Ingeneral, the measurement analysis module 126 may be configured toanalyze the sensor data 118 to determine the measurement data 120.Further, as will be discussed further below, the measurement analysismodule 126 can cooperatively operate with or otherwise leverage acalibration model 128 to convert the measurement data 120 from anautomatic system metric to an agronomy metric. As an example, themeasurement analysis module 126 can perform some or all of method 200 ofFIG. 5. Although the measurement analysis module 126 is described asperforming both generation of automatic field measurement data andconversion of automatic field measurement data to agronomy data, in someembodiments, these operations are performed by separate components ormodules.

Moreover, as shown in FIG. 3, the instructions 116 stored within thememory 112 of the controller 102 may also be executed by theprocessor(s) 110 to implement a calibration model 128. The calibrationmodel 128 can describe or apply a relationship between the automaticsystem metric associated with the automatic field measurement system andan agronomy metric, where the agronomy metric is different from theautomatic system metric.

The calibration model 128 can have a number of different forms orstructures. As one example, the calibration model 128 can perform orinclude a least squares polynomial fit that describes the relationshipbetween the automatic system metric and the agronomy metric according toa polynomial expression. As another example, the calibration model 128can include or use a look up table that provides an agronomy data valuefor each possible field measurement data value. As yet another example,the calibration model 128 can perform or include a Gaussian Processregression that describes the relationship between the automatic systemmetric and the agronomy metric As another example, the calibration model128 can be or include a spline or otherwise piece-wise function thatdescribes the relationship between the automatic system metric and theagronomy metric. As yet another example, the calibration model 128 canconvert field measurement data expressed according to a logarithmicscale into agronomy data expressed according to a linear scale. In yetanother example, the calibration model 128 can perform rounding todiscrete output values (e.g., Input 11.23% to Output 10%).

Referring still to FIG. 3, the instructions 116 stored within the memory112 of the controller 102 may also be executed by the processor(s) 110to implement a control module 129. In general, the control module 129may be configured to adjust the operation of the work vehicle 10 and/orthe implement 12 by controlling one or more components of theimplement/vehicle 12, 10. Specifically, in several embodiments, when theagronomy data values determined by the measurement analysis module 126differ from target or desired values, the control module 129 may beconfigured to adjust the operation of the work vehicle 10 and/or theimplement 12 in a manner designed to modify the outcome of the operationof the work vehicle 10 and/or the implement 12. For instance, when it isdesired to have a percent soil roughness coverage of 30%, the controlmodule 129 may be configured to adjust the operation of the work vehicle10 and/or the implement 12 so as to increase or decrease the amount ofsoil roughness remaining in the field when the estimated percent soilroughness coverage for a given imaged portion of the field (or anaverage estimated percent soil roughness coverage across multiple imagedportions of the field) differs from the target percentage.

In one example, one or more sensors 121 (e.g., imaging devices 104) canbe forward-looking image devices that collect sensor data descriptive ofupcoming portions of the field. The measurement analysis module 126 cananalyze the sensor to determine automatic field measurement data forsuch upcoming portions of the field. The measurement analysis module 126can use the calibration model 128 to convert the automatic fieldmeasurement data into agronomy data. The control module 129 can adjustthe operation of the work vehicle 10 and/or the implement 12 based onthe agronomy data for such upcoming portions of the field. Thus, thesystem 100 can proactively manage various operational parameters of thework vehicle 10 and/or the implement 12 to account for upcoming fieldconditions in upcoming portions of the field. For example, if anupcoming portion of the field has a larger-than-average soil roughnesspercentage, then the controller 102 can, in anticipation of reachingsuch section, modify the operational parameters to account for suchlarger-than-average soil roughness and vice versa for portions withless-than-average soil roughness.

In another example which may be additional or alternative to the exampleprovided above, one or more sensors 121 (e.g., imaging devices 104) canbe rearward-looking image devices that collect sensor data descriptiveof receding portions of the field that the work vehicle 10 and/orimplement 12 has recently operated upon. The measurement analysis module126 can analyze the sensor data to determine automatic field measurementdata for such receding portions of the field. The measurement analysismodule 126 can use the calibration model 128 to convert the automaticfield measurement data into agronomy data. The control module 129 canadjust the operation of the work vehicle 10 and/or the implement 12based on the agronomy data for such receding portions of the field.Thus, the system 100 can reactively manage various operationalparameters of the work vehicle 10 and/or the implement 12 based onobserved outcomes associated with current settings of such operationalparameters. That is, the system 100 can observe the outcome of itscurrent settings and can adjust the settings if the outcome does notmatch a target outcome.

It should be appreciated that the controller 102 may be configured toimplement various different control actions to adjust the operation ofthe work vehicle 10 and/or the implement 12 in a manner that increasesor decreases various field conditions in the field. In one embodiment,the controller 102 may be configured to increase or decrease theoperational or ground speed of the implement 12 to affect an increase ordecrease certain field conditions such as percent crop residue coverand/or soil roughness. For instance, as shown in FIG. 3, the controller102 may be communicatively coupled to both the engine 22 and thetransmission 24 of the work vehicle 10. In such an embodiment, thecontroller 102 may be configured to adjust the operation of the engine22 and/or the transmission 24 in a manner that increases or decreasesthe ground speed of the work vehicle 10 and, thus, the ground speed ofthe implement 12, such as by transmitting suitable control signals forcontrolling an engine or speed governor (not shown) associated with theengine 22 and/or transmitting suitable control signals for controllingthe engagement/disengagement of one or more clutches (not shown)provided in operative association with the transmission 24.

In some embodiments, the implement 12 can communicate with the workvehicle 10 to request or command a particular ground speed and/orparticular increase or decrease in ground speed from the work vehicle10. For example, the implement 12 can include or otherwise leverage anISOBUS Class 3 system to control the speed of the work vehicle 10.

Increasing the ground speed of the vehicle 10 and/or the implement 12may result in a relative increase in the amount of soil roughness and/orcrop residue remaining in the field (e.g., relative to the amountremaining absent such increase in ground speed). Likewise, decreasingthe ground speed of the vehicle 10 and/or the implement 12 may result ina relative decrease in the amount of soil roughness and/or crop residueremaining in the field (e.g., relative to the amount remaining absentsuch decrease in ground speed).

In addition to the adjusting the ground speed of the vehicle/implement10, 12 (or as an alternative thereto), the controller 102 may also beconfigured to adjust an operating parameter associated with theground-engaging tools of the implement 12. For instance, as shown inFIG. 3, the controller 102 may be communicatively coupled to one or morevalves 130 configured to regulate the supply of fluid (e.g., hydraulicfluid or air) to one or more corresponding actuators 56, 58, 60 of theimplement 12. In such an embodiment, by regulating the supply of fluidto the actuator(s) 56, 58, 60, the controller 102 may automaticallyadjust the relative positioning, penetration depth, down force, and/orany other suitable operating parameter associated with theground-engaging tools of the implement 12. Increasing the penetrationdepth or down force of the ground-engaging tools may result in arelative decrease in the amount of soil roughness and/or crop residueremaining in the field (e.g., relative to the amount remaining absentsuch increase in penetration depth or down force). Likewise, decreasingthe penetration depth or down force of the ground-engaging tools mayresult in a relative increase in the amount of soil roughness and/orcrop residue remaining in the field (e.g., relative to the amountremaining absent such decrease in penetration depth or down force).

Referring now to FIG. 4, according to an aspect of the presentdisclosure, the controller 102 can store or include one or morecalibration models 128. In some embodiments, the calibration models 128can be or can otherwise include various machine-learned models such as amachine-learned regression model; a support vector machine; one or moredecision trees; a neural network; and/or other types of models includingboth linear models and non-linear models.

Example machine-learned regression models perform linear regression,polynomial regression, or nonlinear regression. Example machine-learnedregression models can perform simple regression or multiple regression.In some embodiments, a softmax function or layer can be used to squash aset of real values respectively associated with two or more possibleoutputs to a set of real values in the range (0, 1) that sum to one.

Example neural networks include feed-forward neural networks, recurrentneural networks (e.g., long short-term memory recurrent neuralnetworks), convolutional neural networks, or other forms of neuralnetworks.

However, in some embodiments, the calibration models 128 are notmachine-learned but may instead be hand-crafted heuristics or other datastructures that enable a conversion from automatic field measurements toagronomy measurements.

In some embodiments in which the calibration models 128 aremachine-learned, the controller 102 can receive the one or morecalibration models 128 from the machine learning computing system 150over network 180 and can store the one or more calibration models 128 inthe memory 112. The controller 102 can then use or otherwise run the oneor more calibration models 128 (e.g., by processor(s) 110).

The machine learning computing system 150 includes one or moreprocessors 152 and a memory 154. The one or more processors 152 can beany suitable processing device such as described with reference toprocessor(s) 110. The memory 154 can include any suitable storage devicesuch as described with reference to memory 112.

The memory 154 can store information that can be accessed by the one ormore processors 152. For instance, the memory 154 (e.g., one or morenon-transitory computer-readable storage mediums, memory devices) canstore data 156 that can be obtained, received, accessed, written,manipulated, created, and/or stored. In some implementations, themachine learning computing system 150 can obtain data from one or morememory device(s) that are remote from the system 150.

The memory 154 can also store computer-readable instructions 158 thatcan be executed by the one or more processors 152. The instructions 158can be software written in any suitable programming language or can beimplemented in hardware. Additionally, or alternatively, theinstructions 158 can be executed in logically and/or virtually separatethreads on processor(s) 152.

For example, the memory 154 can store instructions 158 that whenexecuted by the one or more processors 152 cause the one or moreprocessors 152 to perform any of the operations and/or functionsdescribed herein.

In some implementations, the machine learning computing system 150includes one or more server computing devices. If the machine learningcomputing system 150 includes multiple server computing devices, suchserver computing devices can operate according to various computingarchitectures, including, for example, sequential computingarchitectures, parallel computing architectures, or some combinationthereof.

In addition or alternatively to the model(s) 128 at the controller 102,the machine learning computing system 150 can include one or morecalibration models 140. For example, the models 140 can be or canotherwise include various calibration models such as any of the examplemodels described above with reference to models 128.

In some embodiments, the machine learning computing system 150 cancommunicate with the controller 102 according to a client-serverrelationship. For example, the machine learning computing system 150 canimplement the calibration models 140 to provide a web service to thecontroller 102. For example, the web service can provide datacalibration as a service.

Thus, calibration models 128 can located and used at the controller 102and/or calibration models 140 can be located and used at the machinelearning computing system 150.

In some implementations, the machine learning computing system 150and/or the controller 102 can train the calibration models 128 and/or140 through use of a trainer 160. The trainer 160 can train thecalibration models 128 and/or 140 using one or more training or learningalgorithms. One example training technique is backwards propagation oferrors (“backpropagation”). Gradient-based or other training techniquescan be used as well.

In some implementations, the trainer 160 can perform supervised trainingtechniques using a set of labeled training data 162. For example, thelabeled training data 162 can include a set of field measurement datathat is labeled with the “correct” agronomy data that is correctlycalibrated to the field measurement data. Thus, the labeled trainingdata can provide examples of inputs and correctly calibrated outputs. Inother implementations, the trainer 160 can perform unsupervised trainingtechniques using a set of unlabeled training data 162.

The trainer 160 can perform a number of generalization techniques toimprove the generalization capability of the models being trained.Generalization techniques include weight decays, dropouts, or othertechniques. The trainer 160 can be implemented in hardware, software,firmware, or combinations thereof.

Furthermore, as indicated above, the calibration models 128 are notrequired to be machine-learned models but can instead be heuristics orother algorithms or structures that enable conversion from automaticfield measurement data to agronomy data, as described herein.

The network(s) 180 can be any type of network or combination of networksthat allows for communication between devices. In some embodiments, thenetwork(s) can include one or more of a local area network, wide areanetwork, the Internet, secure network, cellular network, mesh network,peer-to-peer communication link and/or some combination thereof and caninclude any number of wired or wireless links. Communication over thenetwork(s) 180 can be accomplished, for instance, via a communicationsinterface using any type of protocol, protection scheme, encoding,format, packaging, etc.

FIGS. 3 and 4 illustrate example computing systems that can be used toimplement the present disclosure. Other computing systems can be used aswell. For example, in some implementations, the controller 102 caninclude the trainer 160 and the training dataset 162. In suchimplementations, the calibration models 128 can be both trained and usedlocally at the controller 102. As another example, in someimplementations, the controller 102 is not connected to other computingsystems.

Referring now to FIG. 5, a flow diagram of one embodiment of a method200 for calibrating automatic field measurements is illustrated inaccordance with aspects of the present subject matter. In general, themethod 200 will be described herein with reference to the work vehicle10 and the implement 12 shown in FIGS. 1 and 2, as well as the varioussystem components shown in FIGS. 3 and/or 4. However, it should beappreciated that the disclosed method 200 may be implemented with workvehicles and/or implements having any other suitable configurationsand/or within systems having any other suitable system configuration. Inaddition, although FIG. 5 depicts steps performed in a particular orderfor purposes of illustration and discussion, the methods discussedherein are not limited to any particular order or arrangement. Oneskilled in the art, using the disclosures provided herein, willappreciate that various steps of the methods disclosed herein can beomitted, rearranged, combined, and/or adapted in various ways withoutdeviating from the scope of the present disclosure.

As shown in FIG. 5, at (202), the method 200 may include receiving fieldmeasurement data generated by an automatic field measurement system. Thefield measurement data generated by the automatic field measurementsystem can be expressed according to an automatic system metricassociated with the automatic field measurement system. For example, asindicated above, the measurement analysis module 126 of the controller102 may be configured to generate the automatic field measurement datafrom sensor data. As examples, the field measurement data may be cropresidue cover data descriptive of crop residue in a field and/or soilroughness data.

At (204), the method 200 may include accessing, with the computingdevice, a calibration model that describes a relationship between theautomatic system metric associated with the automatic field measurementsystem and an agronomy metric that is different from the automaticsystem metric. For example, the measurement analysis module 126 canaccess the calibration model 128.

The calibration model accessed at (204) can have a number of differentforms or structures. As one example, the calibration model can performor include a least squares polynomial fit that describes therelationship between the automatic system metric and the agronomy metricaccording to a polynomial expression. As another example, thecalibration model can include or use a look up table that provides anagronomy data value for each possible field measurement data value. Asyet another example, the calibration model can perform or include aGaussian Process regression that describes the relationship between theautomatic system metric and the agronomy metric As another example, thecalibration model can be or include a spline or otherwise piece-wisefunction that describes the relationship between the automatic systemmetric and the agronomy metric. As yet another example, the calibrationmodel can convert field measurement data expressed according to alogarithmic scale into agronomy data expressed according to a linearscale.

In some embodiments, the automatic system metric can include the fieldmeasurement data expressed according to a logarithmic scale while theagronomy metric can include the agronomy data expressed according to alinear scale.

At (206), the method 200 may include using the calibration model toconvert the field measurement data expressed according to the automaticsystem metric into agronomy data expressed according to the agronomymetric. For example, the measurement analysis module 126 can use thecalibration model 128 to convert the field measurement data expressedaccording to the automatic system metric into agronomy data expressedaccording to the agronomy metric.

At (208), the method 200 may include controlling, based at least in parton the agronomy data, an operation of at least one of an agriculturalwork vehicle or implement. For example, as indicated above, the controlmodule 129 of the controller 102 of the disclosed system 100 may beconfigured to control the operation of the work vehicle 10 and/or theimplement 12, such as by controlling one or more components of the workvehicle 10 and/or the implement 12 to allow an operation to be performedwithin the field (e.g., a tillage operation).

In some embodiments, both the automatic field measurement system and thecomputing system implementing method 200 are physically located on-boardthe at least one of the agricultural work vehicle or implement.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they include structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal languages of the claims.

What is claimed is:
 1. A computer-implemented method for calibratingautomatic field measurements, the method comprising: receiving, with acomputing device, field measurement data generated by an automatic fieldmeasurement system, wherein the field measurement data generated by theautomatic field measurement system is expressed according to anautomatic system metric associated with the automatic field measurementsystem; accessing, with the computing device, a calibration model thatdescribes a relationship between the automatic system metric associatedwith the automatic field measurement system and an agronomy metric,wherein the agronomy metric is different from the automatic systemmetric; and using, with the computing device, the calibration model toconvert the field measurement data expressed according to the automaticsystem metric into agronomy data expressed according to the agronomymetric.
 2. The computer-implemented method of claim 1, wherein the fieldmeasurement data is associated with a field and wherein the methodfurther comprises: controlling, with the computing device and based atleast in part on the agronomy data, an operation of at least one of anagricultural work vehicle or implement.
 3. The computer-implementedmethod of claim 2, wherein both the automatic field measurement systemand the computing device are physically located on-board the at leastone of the agricultural work vehicle or implement.
 4. Thecomputer-implemented method of claim 1, wherein: accessing, with thecomputing device, the calibration model comprises accessing, with thecomputing device, a least squares polynomial fit that describes therelationship between the automatic system metric and the agronomy metricaccording to a polynomial expression; and using, with the computingdevice, the calibration model comprises using, with the computingdevice, the least squares polynomial fit to convert the fieldmeasurement data expressed according to the automatic system metric intoagronomy data expressed according to the agronomy metric.
 5. Thecomputer-implemented method of claim 1, wherein: accessing, with thecomputing device, the calibration model comprises accessing, with thecomputing device, a look up table that provides an agronomy data valuefor each possible field measurement data value; and using, with thecomputing device, the calibration model comprises using, with thecomputing device, the look up table to convert the field measurementdata expressed according to the automatic system metric into agronomydata expressed according to the agronomy metric.
 6. Thecomputer-implemented method of claim 1, wherein: accessing, with thecomputing device, the calibration model comprises accessing, with thecomputing device, a Gaussian Process regression that describes therelationship between the automatic system metric and the agronomymetric; and using, with the computing device, the calibration modelcomprises using, with the computing device, the Gaussian Processregression to convert the field measurement data expressed according tothe automatic system metric into agronomy data expressed according tothe agronomy metric.
 7. The computer-implemented method of claim 1,wherein: accessing, with the computing device, the calibration modelcomprises accessing, with the computing device, a spline that describesthe relationship between the automatic system metric and the agronomymetric; and using, with the computing device, the calibration modelcomprises using, with the computing device, the spline to convert thefield measurement data expressed according to the automatic systemmetric into agronomy data expressed according to the agronomy metric. 8.The computer-implemented method of claim 1, wherein the fieldmeasurement data comprises crop residue cover data descriptive of cropresidue in a field.
 9. The computer-implemented method of claim 8,wherein the automatic system metric comprises the field measurement dataexpressed according to a logarithmic scale and wherein the agronomymetric comprises the agronomy data expressed according to a linearscale.
 10. The computer-implemented method of claim 1, wherein the fieldmeasurement data comprises soil roughness data.
 11. A computing systemfor calibrating automatic field measurements, the computing systemcomprising: one or more processors; and one or more non-transitorycomputer-readable media that collectively store: a calibration modelthat describes a relationship between an automatic system metricassociated with an automatic field measurement system and an agronomymetric, wherein the agronomy metric is different from the automaticsystem metric; and instructions that, when executed by the one or moreprocessors, cause the computing system to perform operations, theoperations comprising: receiving field measurement data generated by theautomatic field measurement system, wherein the field measurement datagenerated by the automatic field measurement system is expressedaccording to the automatic system metric associated with the automaticfield measurement system; accessing the calibration model that describesthe relationship between the automatic system metric associated with theautomatic field measurement system and the agronomy metric; and usingthe calibration model to convert the field measurement data expressedaccording to the automatic system metric into agronomy data expressedaccording to the agronomy metric.
 12. The computing system of claim 11,wherein the computing system further comprises the automatic fieldmeasurement system configured to generate the field measurement data.13. The computing system of claim 11, wherein the field measurement datais associated with a field and wherein the operations further comprise:controlling, based at least in part on the agronomy data, an operationof at least one of a work vehicle located in the field or an implementtowed by the work vehicle across the field.
 14. The computing system ofclaim 13, wherein both the automatic field measurement system and thecomputing system are physically located on-board the at least one of thework vehicle or the implement.
 15. The computing system of claim 11,wherein: accessing the calibration model comprises accessing a leastsquares polynomial fit that describes the relationship between theautomatic system metric and the agronomy metric according to apolynomial expression; and using the calibration model comprises usingthe least squares polynomial fit to convert the field measurement dataexpressed according to the automatic system metric into agronomy dataexpressed according to the agronomy metric.
 16. The computing system ofclaim 11, wherein: accessing the calibration model comprises accessing alook up table that provides an agronomy data value for each possiblefield measurement data value; and using the calibration model comprisesusing the look up table to convert the field measurement data expressedaccording to the automatic system metric into agronomy data expressedaccording to the agronomy metric.
 17. The computing system of claim 11,wherein: accessing the calibration model comprises accessing a GaussianProcess regression that describes the relationship between the automaticsystem metric and the agronomy metric; and using the calibration modelcomprises using the Gaussian Process regression to convert the fieldmeasurement data expressed according to the automatic system metric intoagronomy data expressed according to the agronomy metric.
 18. Thecomputing system of claim 11, wherein: accessing the calibration modelcomprises accessing a spline that describes the relationship between theautomatic system metric and the agronomy metric; and using thecalibration model comprises using the spline to convert the fieldmeasurement data expressed according to the automatic system metric intoagronomy data expressed according to the agronomy metric.
 19. Thecomputing system of claim 18, wherein the automatic system metriccomprises the field measurement data expressed according to alogarithmic scale and wherein the agronomy metric comprises the agronomydata expressed according to a linear scale.
 20. An agricultural workvehicle or agricultural implement, comprising: an automatic fieldmeasurement system configured to generate field measurement datadescriptive of an agricultural field, wherein the field measurement datagenerated by the automatic field measurement system is expressedaccording to an automatic system metric associated with the automaticfield measurement system; and a calibration computing system configuredto: receive the field measurement data generated by the automatic fieldmeasurement system; access a calibration model that describes arelationship between the automatic system metric associated with theautomatic field measurement system and an agronomy metric, wherein theagronomy metric is different from the automatic system metric; and usethe calibration model to convert the field measurement data expressedaccording to the automatic system metric into agronomy data expressedaccording to the agronomy metric.